Apparatus and method for generating a vehicle maintenance activity

ABSTRACT

An apparatus and method for generating a vehicle maintenance activity, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive image data, identify at least an indicator from the image data, receive vehicle data as a function of the indicator, classify the vehicle data to a plurality of vehicle data categories, and generate a vehicle maintenance activity as a function of the classified vehicle data.

FIELD OF THE INVENTION

The present invention generally relates to the field of datarecognition. In particular, the present invention is directed to anapparatus and method for generating a vehicle maintenance activity.

BACKGROUND

Current methods of verifying information at an entry point areinsufficient. There is a need for a method wherein authorization at anentry point allows access to a plurality of data to provide apersonalized experience upon entry.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating a vehicle maintenanceactivity, wherein the apparatus includes at least a processor and amemory communicatively connected to the at least a processor, the memorycontaining instructions configuring the at least a processor to receiveimage data, identify at least an indicator from the image data, receivevehicle data as a function of the indicator, classify the vehicle datato a plurality of vehicle data categories, and generate a vehiclemaintenance activity as a function of the classified vehicle data.

In another aspect, a method for generating a vehicle maintenanceactivity, wherein the method includes receiving, by a computing device,image data, identifying, by the computing device, at least an indicatorfrom the image data, receiving, by the computing device, vehicle data asa function of the indicator, classifying, by the computing device, thevehicle data to a plurality of vehicle data categories, and generating,by the computing device, a vehicle maintenance activity as a function ofthe classified vehicle data.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1A is a block diagram illustrating an apparatus for generating avehicle maintenance activity;

FIGS. 1B-C is an illustration of exemplary embodiments of an apparatusfor generating a vehicle maintenance activity as a vehicle verificationsystem;

FIG. 2 is a block diagram illustrating a Chatbot;

FIG. 3 is a block diagram of an exemplary machine-learning process;

FIG. 4 is a diagram of an exemplary embodiment of neural network;

FIG. 5 is a diagram of an exemplary embodiment of a node of a neuralnetwork;

FIG. 6 is a diagram of an of fuzzy set comparison;

FIG. 7 is a flow diagram illustrating an exemplary method for generatinga vehicle maintenance activity; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for generating a vehicle maintenance activity. In anembodiment, a vehicle maintenance activity may be a carwash setting.

Aspects of the present disclosure can be used to verify a vehicle and/ordriver at an entry point of facility, such as a carwash, wherein, acomputing device is configured to generate a personalized vehiclemaintenance activity based on the data related to the driver of vehicle.

Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1A, an exemplary embodiment of an apparatus 100for generating a vehicle maintenance activity is illustrated. Apparatus100 includes a processor 104. Processor 104 may include, withoutlimitation, any processor described in this disclosure. Apparatus 100include a memory communicatively connected to the at least a processor,the memory containing instructions configuring the at least a processorto carry out the generating process. As used in this disclosure,“communicatively connected” means connected by way of a connection,attachment or linkage between two or more related which allows forreception and/or transmittance of information therebetween. For example,and without limitation, this connection may be wired or wireless, director indirect, and between two or more components, circuits, devices,systems, and the like, which allows for reception and/or transmittanceof data and/or signal(s) therebetween. Data and/or signals therebetweenmay include, without limitation, electrical, electromagnetic, magnetic,video, audio, radio and microwave data and/or signals, combinationsthereof, and the like, among others. A communicative connection may beachieved, for example and without limitation, through wired or wirelesselectronic, digital or analog, communication, either directly or by wayof one or more intervening devices or components. Further, communicativeconnection may include electrically coupling or connecting at least anoutput of one device, component, or circuit to at least an input ofanother device, component, or circuit. For example, and withoutlimitation, via a bus or other facility for intercommunication betweenelements of a computing device. Communicative connecting may alsoinclude indirect connections via, for example and without limitation,wireless connection, radio communication, low power wide area network,optical communication, magnetic, capacitive, or optical coupling, andthe like. In some instances, the terminology “communicatively coupled”may be used in place of communicatively connected in this disclosure.Processor 104 and memory may be included in a computing device 108.Computing device 108 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. computing device 108 may include,be included in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. computing device 108 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. computing device108 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device108 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.computing device 108 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. computing device 108 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. computing device 108 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. computing device 108 may beimplemented, as a non-limiting example, using a “shared nothing”architecture.

With continued reference to FIG. 1A, computing device 108 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 108 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. computing device 108 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1A, computing device 108 is configured toreceive image data 116. As used in this disclosure, “image data” isinformation representing at least a physical scene, space, and/orobject. In some cases, image data may be generated by a camera. “Imagedata” may be used interchangeably through this disclosure with “image,”where image is used as a noun. Image data 116 may relate to an image ofvehicle. An image may be optical, such as without limitation where atleast an optic is used to generate an image of an object. An image maybe material, such as without limitation when film is used to capture animage. An image may be digital, such as, without limitation, whenrepresented as a bitmap. In a non-limiting example, plurality of imagedata 116 may illustrate various components and/or features of thevehicle such as vehicle's body, color, make, model, license plate, andthe like. In some cases, plurality of image data 116 may be captured byplurality of image capturing device, as still images, or frames from avideo stream. In other cases, plurality of image data 116 may be takenas a “burst” of vehicle images by a plurality of image capturing devices112, as a video feed including a live-streamed video of the vehicle. A“burst” of vehicle images, as described herein, is a set of images of asingle object, such as the vehicle, taken in rapid succession. A burstmay be performed by repeated manually actuated image captures, or may bean “automated burst,” defined as a set of images that are automaticallytriggered by plurality of image capturing devices 112; an automatedburst may be initiated by a manual actuation of, for example, withoutlimitation, a camera button while in an automated burst mode configuringat least an image capture device of plurality of image capturing devices112 and/or any computing device 108 to perform and/or command automatedburst upon a manual actuation, or may be triggered by an automatedprocess and/or module such as a program, hardware component,application, a command or instruction from a remote device, or the like.

Still referring to FIG. 1A, apparatus may receive image data 116 from aplurality of image capturing devices 112. As used in this disclosure, an“image capturing device” is a device that is capable of acquiring visualinformation in a form of digital images or videos. In an embodiment,each image capturing device of plurality of image capturing devices 112may include a usage of a photosensitive element. In a non-limitingexample, a plurality of image capturing devices 112 may include aplurality of cameras. A “camera,” as described herein, is a device thatis configured to sense electromagnetic radiation, such as withoutlimitation visible light, and generate an image representing theelectromagnetic radiation. In some cases, the at least a camera mayinclude one or more optics. For the purposes of this disclosure, an“optic” is a device that focuses and directs electromagnetic radiationto a target area. Exemplary non-limiting, the optics may includespherical lenses, aspherical lenses, reflectors, polarizers, filters,windows, aperture stops, and the like. In some cases, the at least acamera may include an image sensor. Exemplary non-limiting, the imagesensors may include digital image sensors, such as without limitationcharge-coupled device (CCD) sensors and complimentarymetal-oxide-semiconductor (CMOS) sensors, chemical image sensors, andanalog image sensors, such as without limitation film. In some cases,the at least a camera may be sensitive within a non-visible range ofelectromagnetic radiation, such as without limitation infrared. Imagecapturing device 112 may include embodiments as disclosed in U.S. patentapplication Ser. No. 18/195,537, filed on May 10, 2023, entitled“APPARATUS AND METHOD FOR AUTOMATIC LICENSE PLATE RECOGNITION OF AVEHICLE,” the entirety of which is incorporated as a reference.

With continued reference to FIG. 1A, an exemplary image capturing device112 may include an OpenMV Cam H7 from OpenMV, LLC of Atlanta, Georgia,U.S.A. OpenMV Cam includes a small, low power, microcontroller whichallows execution of processes. OpenMV Cam comprises an ARM Cortex M7processor and a 640×480 image sensor operating at a frame rate up to 150fps. OpenMV Cam may be programmed with Python using a RemotePython/Procedure Call (RPC) library. OpenMV CAM may be used to operateimage classification and segmentation models, such as without limitationby way of TensorFlow Lite; detect motion, for example by way of framedifferencing algorithms; detect markers, for example blob detection;detect objects, for example face detection; track eyes; detectionpersons, for example by way of a trained machine learning model; detectcamera motion, detect and decode barcodes; capture images; and recordvideo.

Still referring to FIG. 1A, image capturing device 112 may be equippedwith a machine vision system. A machine vision system may use imagesfrom at least a camera, to make a determination about a scene, space,and/or object. For example, in some cases a machine vision system may beused for world modeling or registration of objects within a space. Insome cases, registration may include image processing, such as withoutlimitation object recognition, feature detection, edge/corner detection,and the like. Non-limiting example of feature detection may includescale invariant feature transform (SIFT), Canny edge detection, ShiTomasi corner detection, and the like. In some cases, registration mayinclude one or more transformations to orient a camera frame (or animage or video stream) relative a three-dimensional coordinate system;exemplary transformations include without limitation homographytransforms and affine transforms. In an embodiment, registration offirst frame to a coordinate system may be verified and/or correctedusing object identification and/or computer vision, as described above.For instance, and without limitation, an initial registration to twodimensions, represented for instance as registration to the x and ycoordinates, may be performed using a two-dimensional projection ofpoints in three dimensions onto a first frame, however. A thirddimension of registration, representing depth and/or a z axis, may bedetected by comparison of two frames; for instance, where first frameincludes a pair of frames captured using a pair of cameras (e.g.,stereoscopic camera also referred to in this disclosure asstereo-camera), image recognition and/or edge detection software may beused to detect a pair of stereoscopic views of images of an object; twostereoscopic views may be compared to derive z-axis values of points onobject permitting, for instance, derivation of further z-axis pointswithin and/or around the object using interpolation. This may berepeated with multiple objects in field of view, including withoutlimitation environmental features of interest identified by objectclassifier and/or indicated by an operator. In an embodiment, x and yaxes may be chosen to span a plane common to two cameras used forstereoscopic image capturing and/or an xy plane of a first frame; aresult, x and y translational components and ϕ may be pre-populated intranslational and rotational matrices, for affine transformation ofcoordinates of object, also as described above. Initial x and ycoordinates and/or guesses at transformational matrices mayalternatively or additionally be performed between first frame andsecond frame, as described above. For each point of a plurality ofpoints on object and/or edge and/or edges of object as described above,x and y coordinates of a first stereoscopic frame may be populated, withan initial estimate of z coordinates based, for instance, on assumptionsabout object, such as an assumption that ground is substantiallyparallel to an xy plane as selected above. Z coordinates, and/or x, y,and z coordinates, registered using image capturing and/or objectidentification processes as described above may then be compared tocoordinates predicted using initial guess at transformation matrices; anerror function may be computed using by comparing the two sets ofpoints, and new x, y, and/or z coordinates, may be iteratively estimatedand compared until the error function drops below a threshold level. Insome cases, a machine vision system may use a classifier, such as anyclassifier described throughout this disclosure.

With continued reference to FIG. 1A, in some embodiments, plurality ofimage capturing devices 112 may capture two or more perspectives for usein three-dimensional (3D) reconstruction. Plurality of image capturingdevices 112 may include a stereo-camera. As used in this disclosure, a“stereo-camera” is a camera that senses two or more images from two ormore vantages. As used in this disclosure, a “vantage” is a location ofa camera relative a scene, space and/or object which the camera isconfigured to sense. In some cases, a stereo-camera may determine depthof an object in a scene as a function of parallax. As used in thisdisclosure, “parallax” is a difference in perceived location of acorresponding object in two or more images. An exemplary stereo-cameramay include TaraXL from e-con Systems, Inc of San Jose, California. TheTaraXL may include a USB 3.0 stereo-camera which is optimized forNVIDIA® Jetson AGX Xavier™/Jetson™ TX2 and NVIDIA GPU Cards. TheTaraXL's accelerated Software Development Kit (TaraXL SDK) may becapable of doing high quality 3D depth mapping of WVGA at a rate of upto 60 frames per second. The TaraXL may be based on MT9V024 stereosensor from ON Semiconductor. Additionally, the TaraXL may include aglobal shutter, houses 6 inertial measurement units (IMUs), and mayallow mounting of optics by way of an S-mount lens holder. The TaraXLmay operate at depth ranges of about 50 cm to about 300 cm. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various types of cameras that may be used for thedisclosure.

With continued reference to FIG. 1A, in some embodiments, plurality ofimage capturing devices 112 may include at least a photodetector. Forthe purposes of this disclosure, a “photodetector” is any device that issensitive to light and thereby able to detect light. In someembodiments, the at least a photodetector may be implemented in acamera. As a non-limiting example, the at least a photodetector mayconvert the light into electrical signals that can be processed by thecamera's electronics to create an image. In some embodiments, the atleast a photodetector may be implemented in the LiDAR system asdescribed below. As a non-limiting example, the at least a photodetectormay receive laser light from a light detecting and ranging (LiDAR)system that reflects off an object, such as but not limited to avehicle, or environment and may convert it into an electrical signal,such as but not limited to LiDAR data of plurality of image data 116.

Still referring to FIG. 1A, computing device 108 is configured toreceive an indicator 120 from the image data 116. As used in thisdisclosure, an “indicator” is a symbol or plurality of symbolsconfigured to identify an object or entity. A symbol may include a markor character used as a conventional representation of an object,function, or process, and the like. In some embodiments, indicator mayinclude a symbol or plurality of symbols located on a license plate,also referred to as a vehicle credential plate or a vehicle credential,of a vehicle. In a non-limiting example, indicator 120 may include acharacter such as, without limitation, a letter, a number, or a specialcharacter. In a non-limiting example, image data 116 may contain andimage of a vehicle license plate, wherein the plurality of indicator 120may include a combination of letters, numbers, and/or specialcharacters, horizontal or vertical stacked in single, or multiple rowswithin license plate region, such as the license plate number. In somecases, each indicator 120 of plurality of indicator 120 may include asame/different font size (i.e., 6 inches by 12 inches, 520 mm by 110 mm.372 mm by 134 mm, and/or the like) or a same/different font style (e.g.,standard, embossed, italic, condensed, gothic, retro, and/or the like),In some cases, each indicator 120 of plurality of indicator 120 may bein a same/different font color (e.g., white, green, blue, yellow, black,red, and/or the like) Additionally, or alternatively, indicator 120 mayinclude a presence of other elements within the license plate region,such as, without limitation, jurisdiction name, logo/emblem/symbol,registration sticker, hologram, or the like. Further, indicator 120 maybe in a computer readable format; for instance, and without limitation,indicator 120 may be expressed solely in textural/numerical format.Indicator 120 may include embodiments as disclosed in U.S. patentapplication Ser. No. 18/195,537.

Still referring to FIG. 1A, computing device 108 may identify anindicator 120 using optical character recognition techniques, in someembodiments, optical character recognition or optical character reader(OCR) includes automatic conversion of images of written (e.g., typed,handwritten or printed text) into machine-encoded text. In some cases,recognition of at least a keyword from an image component may includeone or more processes, including without limitation optical characterrecognition (OCR), optical word recognition, intelligent characterrecognition, intelligent word recognition, and the like. In some cases,OCR may recognize written text, one glyph or character at a time. Insome cases, optical word recognition may recognize written text, oneword at a time, for example, for languages that use a space as a worddivider. In some cases, intelligent character recognition (ICR) mayrecognize written text one glyph or character at a time, for instance byemploying machine learning processes. In some cases, intelligent wordrecognition (IWR) may recognize written text, one word at a time, forinstance by employing machine learning processes.

Still referring to FIG. 1A, in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information can make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 1A, in some cases, OCR processes may employpre-processing of image component. Pre-processing process may includewithout limitation de-skew, de-speckle, binarization, line removal,layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to image component to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component. In some cases, binarization may berequired for example if an employed OCR algorithm only works on binaryimages. In some cases. a line removal process may include removal ofnon-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component.

Still referring to FIG. 1A, in some embodiments an OCR process willinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some case, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component. Matrix matching may also rely on a storedglyph being in a similar font and at a same scale as input glyph. Matrixmatching may work best with typewritten text.

Still referring to FIG. 1A, in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into features. Exemplary non-limiting features mayinclude corners, edges, lines, closed loops, line direction, lineintersections, and the like. In some cases, feature extraction mayreduce dimensionality of representation and may make the recognitionprocess computationally more efficient. In some cases, extracted featurecan be compared with an abstract vector-like representation of acharacter, which might reduce to one or more glyph prototypes. Generaltechniques of feature detection in computer vision are applicable tothis type of OCR. In some embodiments, machine-learning process likenearest neighbor classifiers (e.g., k-nearest neighbors algorithm) canbe used to compare image features with stored glyph features and choosea nearest match. OCR may employ any machine-learning process describedin this disclosure, for example machine-learning processes as describedthroughout this disclosure. Exemplary non-limiting OCR software includesCuneiform and Tesseract. Cuneiform is a multi-language, open-sourceoptical character recognition system originally developed by CognitiveTechnologies of Moscow, Russia. Tesseract is free OCR softwareoriginally developed by Hewlett-Packard of Palo Alto, California, UnitedStates.

Still referring to FIG. 1A, in some cases, OCR may employ a two-passapproach to character recognition. Second pass may include adaptiverecognition and use letter shapes recognized with high confidence on afirst pass to recognize better remaining letters on the second pass. Insome cases, two-pass approach may be advantageous for unusual fonts orlow-quality image components where visual verbal content may bedistorted. Another exemplary OCR software tool include OCRopus. OCRopusdevelopment is led by German Research Centre for Artificial Intelligencein Kaiserslautern, Germany. In some cases, OCR software may employneural networks as described in this disclosure.

Still referring to FIG. 1A, in some cases, OCR may includepost-processing. For example, OCR accuracy can be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a priori knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

Still referring to FIG. 1A, computing device 108 is configured toreceive vehicle data 128 as function of the indicator 120. “Vehicledata,” as used herein, is information related to a vehicle. Vehicle data128 may include a user identification. “User identification,” as usedherein, is information related to a driver of the vehicle. The drivermay be the owner or authorized user of the vehicle. An authorized usermay be a user listed on legal documents, such as vehicle insurance, orusers listed on an account, such as a client account of a carwashfacility. As a non-limiting example, the driver information may includename, gender, date of birth, residency, religion, driver history,occupation, family, billing information, contact information, emergencycontact, driver's license, state ID, photo ID, billing information suchas but not limited to payment method, payment information, paymenthistory, and the like.

Still referring to FIG. 1A, vehicle data 128 may include a vehicleidentification. A “vehicle identification,” as used herein, isinformation describing a vehicle. As a non-limiting example, the vehicleinformation of the vehicle data 128 may include the make, model, modelversion, model year, manufacturer contact information, country ofmanufacturer, body type, color, coating, steering type, wheel type, tiresize, tire type, number of wheels, standard seat number, optional seatnumber, engine specifications, engine capacity, fuel type, fuel tankcapacity, average fuel consumption, maximum permissible weight, vehicleheight, vehicle length, vehicle width, vehicle status, such as but notlimited to damage status, presence of vehicle accessories, titlerecords, theft records, accident records, insurance records, vehicle ID,interior fabric, license plate number—an alphanumeric credential, andthe like. In some embodiments alphanumeric credential may include alicense plate number. An “alphanumeric credential,” for the purposes ofthis disclosure, is an key including both numbers and letter thatidentifies an object or entity.

Still referring to FIG. 1A, vehicle data 128 may include serviceinformation. “Service information,” as used herein, is informationrelated to past service performed on a vehicle. As a non-limitingexample, the service information may include a service history such asbut not limited to wash service history, vacuum service history, tireservice history, ceramic coating service history, and the like. As anon-limiting example, the service information may a include driver'sservice preference such as but not limited to washing preference for acarwash. Wash preferences may include soap type, water temperature,number of rinse and the like. In some embodiments, a service preferencemay relate to a digital content preference regarding what type ofdigital content should be displayed onto a vehicle during the carwashbase. A digital content preference may be based on age, interests, andthe like. The user identification may be associated with the serviceinformation. As in, when received by the computing device 108, theservice information is correlated to the vehicle owned by a particularuser. In some embodiments, the service information may be updated basedon a generated vehicle maintenance activity 144 as described furtherbelow.

Still referring to FIG. 1A, vehicle data 128 may include feeinformation. “Fee information,” a used herein, is a pecuniary valueassociated with a service performed on a vehicle. Fee information mayinclude in prices, discounts, promotional prices, and the like. In someembodiments, fee information may include the duration ofpromotional/discounted prices, promo/discount codes associated with aservice, and the like, For example, a price of a standard carwash may be$12, wherein a discounted price may be $9. Vehicle data 128 may bereceived from a vehicle database. A “vehicle database,” as used herein,is a data structure populated with information related to a vehicle.Databases, as described throughout this disclosure, may be implemented,without limitation, as a relational database, a key-value retrievaldatabase such as a NOSQL database, or any other format or structure foruse as a database that a person skilled in the art would recognize assuitable upon review of the entirety of this disclosure. Databases mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Databases may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure.

Still referring to FIG. 1A, vehicle data 128 may be received from aremote computing device. A “remote computing device,” as used herein isa computing device operated by a third party. A third party may refer toa driver of a vehicle or the operator of a business. A remote computingdevice may be communicatively connected to computing device 108. In someembodiments, vehicle data 128 may be received utilizing a chatbotthrough a graphical user interface. A “graphical user interface (GUI),”as used herein, is a graphical form of user interface that allows usersto interact with electronic devices. In some embodiments, GUI mayinclude icons, menus, other visual indicators, or representations(graphics), audio indicator such as primary notation, and displayinformation and related user controls. In some embodiments, a thirdparty may submit user identification, vehicle identification,information, fee information, service preference, and the like throughGUI, such as submitting documents or text or audio input of data. Insome embodiments, vehicle data 128 may be received through a chatbotutilizing GUI. As used in the current disclosure, a “chatbot” is acomputer program designed to simulate conversation with users. A chatbotoperating on a GUI may prompt questions for a third party asking forvehicle data 128.

Still referring to FIG. 1A, computing device 108 may retrieve vehicledata 128 based on one or more indicators 120. For example, the licenseplate number of a vehicle may be identified then matched to vehicle data128 containing the same license plate number. Computing device 108 mayretrieve vehicle data 128 based on one more indicators 120 utilizing amachine-learning model such as a classifier, as described further below.An indicator classifier 124 may be configured to receive image data 116and indicator 120 as an input and output correlating vehicle data 128.An indicator classifier 124 training data set may correlate indicator120 to vehicle data 128.

Still referring to FIG. 1A, computing device 108 is configured toclassify the vehicle data to a plurality of vehicle data categories 136.A “vehicle data category,” as used herein is a classification of vehicledata 128 to a type of vehicle related information. A vehicle datacategory 136 may include a user ID category, vehicle ID category,service information category, service preference category and the like.Computing device 108 may classify vehicle data 128 using amachine-learning model, such as a vehicle data classifier 132. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A vehicle data classifier 132 may be configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, found to be close under a distancemetric as described below, or the like. Computing device 108 and/oranother device may generate classifiers, as described throughout thisdisclosure, using a classification algorithm, defined as a processeswhereby a computing device 108 derives a classifier from training data.A vehicle data classifier training data set may include training datacorrelating elements of user identification information, vehicle data128, and/or indicators 120 to a vehicle data group. Vehicle dataclassifier 132 may be configured to receive vehicle data 128 as an inputand output a plurality of vehicle datum classified to vehicle datacategories 136.

Still referring to FIG. 1A, classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Computing device 108 may be configured togenerate classifiers as used in this disclosure using a Naïve Bayesclassification algorithm. Naïve Bayes classification algorithm generatesclassifiers by assigning class labels to problem instances, representedas vectors of element values. Class labels are drawn from a finite set.Naïve Bayes classification algorithm may include generating a family ofalgorithms that assume that the value of a particular element isindependent of the value of any other element, given a class variable.Naïve Bayes classification algorithm may be based on Bayes Theoremexpressed as P(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability ofhypothesis A given data B also known as posterior probability; P(B/A) isthe probability of data B given that the hypothesis A was true; P(A) isthe probability of hypothesis A being true regardless of data also knownas prior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 108 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 108 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1A, computing device 108 may beconfigured to generate classifiers as used in this disclosure using aK-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm”as used in this disclosure, includes a classification method thatutilizes feature similarity to analyze how closelyout-of-sample-features resemble training data to classify input data toone or more clusters and/or categories of features as represented intraining data; this may be performed by representing both training dataand input data in vector forms, and using one or more measures of vectorsimilarity to identify classifications within training data, and todetermine a classification of input data. K-nearest neighbors algorithmmay include specifying a K-value, or a number directing the classifierto select the k most similar entries training data to a given sample,determining the most common classifier of the entries in the database,and classifying the known sample; this may be performed recursivelyand/or iteratively to generate a classifier that may be used to classifyinput data as further samples. For instance, an initial set of samplesmay be performed to cover an initial heuristic and/or “first guess” atan output and/or relationship, which may be seeded, without limitation,using expert input received according to any process as describedherein. As a non-limiting example, an initial heuristic may include aranking of associations between inputs and elements of training data.Heuristic may include selecting some number of highest-rankingassociations and/or training data elements.

With continued reference to FIG. 1A, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)} where aiis attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.

Still referring to FIG. 1A, in some embodiments, computing device 108may be configured to perform a verification function based on theimaging data and vehicle data 128 received. Computing device 108 mayverify indicator 120 or other information obtained from image data 116and reference the vehicle data 128 to verify that at least an indicator120 correlates to an element of vehicle data 128. For example, when acar approaches as car wash entrance, imaging device may capture apicture of the vehicle indicting the license plate, vehicle model,vehicle make, vehicle color, and the like. The license plate numberobtained from the license plate may be checked against the license platenumber retrieved from vehicle data 128. If the license plate numbers donot match then access to the car wash may be denied. In some cases,computing device 108 may be configured to verify two or more parameters,such as the license plate and a photo of the driver, of the licenseplate, and the make/model/color of the vehicle, and the like. In someembodiments, computing device 108 may be configured to generate a fraudalert and transmit the fraud alert to a remote computing device operatedby a third party. For example, the manager of the carwash may receivethe alert along with the authorized driver of the vehicle. A “fraudalert,” as used herein is a notification of denied access. The fraudalert may contain information that could not be verified or failed tomatch vehicle. For example, the fraud alert may contain the incorrectlicense plate number, incorrect make/model/color of the vehicle and thelike. Computing device 108 may perform the verification function using amachine-learning model, such as a classifier as described above. In someembodiments, verification may be performed using optical characterrecognition, as described above.

Still referring to FIG. 1A, computing device 108 is configured togenerate a vehicle maintenance activity 144 as a function of theclassified vehicle data 128. A “vehicle maintenance activity,” as usedherein, is a service to be performed on a vehicle. Vehicle maintenanceactivity 144 may include a type of carwash service, such a wash service,digital content service, vacuum service, drying service, ceramic coatingservice, and the like. Vehicle service activities 144 may be uniquelygenerated depending on vehicle data 128 and/or the plurality of vehicledata categories 136. For example, a wash service may particularize thetype and quantity of soap used and pre-soak time during the service. Inanother example, vehicle maintenance activity 144 may include providingpersonalized animation during a carwash as a function of the digitalcontent preference. In another example, vehicle maintenance activity 144may be uniquely generated based on vehicle data 128, such as, thevehicle height, vehicle length, vehicle width, the vehicle paintcomposition, and the like. Such personalization of vehicle maintenanceactivity 144 may be based on the service information of vehicle data128, as described above.

Still referring to FIG. 1A, computing device 108 may generate a vehiclemaintenance activity 144 using a machine-learning model, such a serviceclassifier 140, when personalization of vehicle maintenance activity 144is dependent on one vehicle data category 136 of the plurality of datacategories. Service classifier 140 may receive a vehicle data 128classifier output, such as a vehicle data category 136, as an input andoutput vehicle maintenance activity 144. Service classifier 140 may betrained using service activity training data set correlating vehicledata 128 to a plurality of vehicle maintenance activity 144 associatedwith a vehicle data category 136. For example, a vehicle identificationcategory highlighting the make and model of a vehicle may be correlatedto a particular wash service, wax service, and the like. Computingdevice 108 may generate vehicle maintenance activity 144 dependent on aplurality of inputs obtained from vehicle data 128 and vehicle datacategories 136 using a fuzzy set inference system, as described furtherbelow. For example, classified vehicle data 128 may be represented afuzzy set compared and ranked against a plurality of vehicle maintenanceactivity 144 fuzzy sets to determine an optimal vehicle maintenanceactivity 144 to generate. Optimal may refer to a certain degree of matchand/or benefit a vehicle service may provide compared to the classifiedvehicle data 128.

Still referring to FIG. 1 , apparatus 100 may be further configured totrack a plurality of vehicle maintenance activities 144 performed on avehicle. In some embodiments, plurality of vehicle maintenanceactivities may be tracked during a predetermined time frame for purposessuch as billing. A predetermined time frame may be a day, month, year,and the like. In an embodiment, computing device 108 may update vehicledata 128 with vehicle maintenance activity 144 and subsequently generateand iteratively update a pecuniary record. A “pecuniary record,” as usedherein, is a data structure containing invoice related information. Forexample, a pecuniary record may be an invoice containing all pastservices performed on a vehicle and the fee information, as describedabove, associated with it. The pecuniary record may contain billinginformation of a driver or proprietor of a vehicle. Billing informationmay be received from vehicle data 128 as described above. In someembodiments, pecuniary record may include receipt of payment, credit,discounts, and the like. Pecuniary record may also include paymentsowed, reimbursed, and the like. Computing device 108 may receive noticeof a payment through a GUI/UI and/or a chatbot as described though thisdisclosure. For example, in carwash facility embodiment, a vehicle maydrive up to the entrance of a carwash, wherein a driver may have theoption to insert a payment card into a remote payment devicecommunicatively connected to computing device 108. Notice of payment maybe a component of vehicle data 128.

Still referring to FIG. 1 , computing device 108 may generate apecuniary record using a pecuniary classifier configured to receivevehicle data after generation or completion of vehicle maintenanceactivity 144 and output the pecuniary record. Pecuniary classifier mayinclude classifiers as described above. Pecuniary classifier may betrained by a pecuniary training data set correlating vehicle maintenanceactivities, indicators, notices of payment and the like to a pecuniaryrecord. Computing device 108 may transmit the pecuniary record at theend of a predetermined time period to a remote computing device operatedby a third party as described above.

With continued reference to FIG. 1 , in some embodiments, computingdevice 108 may associate vehicle maintenance activity 144 to a pecuniaryrecord as a function of indicator 120 and/or vehicle data 128. In someembodiments, computing device 108 may look up an existing pecuniaryrecord in a pecuniary record look up table. Pecuniary records inpecuniary record look up table may be associated with indicators 120 oraspects of vehicle data 128. Indicators 120 or aspects of vehicle data128 may be used to find an existing pecuniary record in pecuniary recordlook up table associated with a vehicle or user. Computing device 108may update existing pecuniary record as a function of vehiclemaintenance activity 144. As a non-limiting example, computing device108 may record that vehicle maintenance activity 144 was performed on avehicle within pecuniary record. As a non-limiting example, computingdevice 108 may add a new amount owed by a user or vehicle, or may updatethe amount owed by the user or vehicle in pecuniary record.

Referring now to FIG. 1B-C, an exemplary embodiment of apparatus 100 asa vehicle verification system is illustrated. Apparatus 100 may be usedto verify image data 116 associated with a vehicle 152. A “vehicle,” asued herein, is apparatus for human or cargo transportation. A vehiclemay include a car, truck, cart, and the like. FIG. 1B-C is an exemplaryillustration of image capturing device of apparatus 100. The imagecapturing device depicted may scan for the credentials 148 of thevehicle 152. A credential may refer to a form of identification, such asa license plate of a vehicle. A credential may be an indicator 120 orimage data 116 directly correlated to the identification of a vehicle ordriver. In an embodiment, a credential 148 may be used to validate ofthe user's identity or the vehicles identity. A vehicle 152 may includeany means by which someone or something may be transported. As anon-limiting example, the vehicle 152 may include a car, SUV, sedan,hatchback, sports car, ATV, go cart, truck, bus, motorcycle, bicycle,watercraft, aircraft, snowcraft, and the like. Some vehicles 152 may beconfigured to have multiple credentials 148 that are associated with it.In a non-limiting, example a vehicle 152 may be configured to have twocredentials associated with a front and a rear license plate,respectively. In another non-limiting example, a first credential may beassociated with the vehicle identification number associated with thevehicle, while the second credential comprises an RFID Tag. Examples ofa credential 148 may include but is not limited to an RFID Tag, licenseplate, vehicle identification number, driver's license, key card, andthe like. In an embodiment, a credential 148 may be located on thedashboard, rearview mirror, front license plate, rear license plate,front windshield, rear windshield, driver's side windows, passenger'sside windows, and the like of the vehicle 152. Apparatus 100 may bepositioned according to the location of the credential. In someembodiments, image capturing device 112 may be mounted in an elevatedposition. In other embodiments, image capturing device 112 may bemounted on the left of right side of the vehicle. Image capturing device112 may be mounted at or near the height of the vehicle 152, as depictedin FIG. 1B-C. FIG. 1B may depict the use of multiple image capturingdevice 112 to verify the credentials associated with the vehicle 152.FIG. 1C may depict an exemplary embodiment of apparatus 100 withmultiple view windows 108. The embodiment of apparatus 100 depicted inFIG. 1C may include multiple image capturing device 112 within onewaterproof housing. Each of these image capturing device 112 may bealigned with a separate view window. Image capturing device 112 may beconfigured to be rotatably mounted. As used in the current disclosure,“rotatably mounted” is being securely mounted in a location whileallowing for rotation along at least one axis.

Referring to FIG. 2 , a chatbot system 200 is schematically illustrated.According to some embodiments, a user interface 204 may be communicativewith a computing device 208 that is configured to operate a chatbot. Insome cases, user interface 204 may be local to computing device 208.Alternatively or additionally, in some cases, user interface 204 mayremote to computing device 208 and communicative with the computingdevice 208, by way of one or more networks, such as without limitationthe internet. Alternatively or additionally, user interface 204 maycommunicate with user device 208 using telephonic devices and networks,such as without limitation fax machines, short message service (SMS), ormultimedia message service (MMS). Commonly, user interface 204communicates with computing device 208 using text-based communication,for example without limitation using a character encoding protocol, suchas American Standard for Information Interchange (ASCII). Typically, auser interface 204 conversationally interfaces a chatbot, by way of atleast a submission 212, from the user interface 208 to the chatbot, anda response 216, from the chatbot to the user interface 204. In manycases, one or both of submission 212 and response 216 are text-basedcommunication. Alternatively or additionally, in some cases, one or bothof submission 212 and response 216 are audio-based communication.

Continuing in reference to FIG. 2 , a submission 212 once received bycomputing device 208 operating a chatbot, may be processed by aprocessor 220. In some embodiments, processor 220 processes a submission212 using one or more of keyword recognition, pattern matching, andnatural language processing. In some embodiments, processor employsreal-time learning with evolutionary algorithms. In some cases,processor 220 may retrieve a pre-prepared response from at least astorage component 224, based upon submission 212. Alternatively oradditionally, in some embodiments, processor 220 communicates a response216 without first receiving a submission 212, thereby initiatingconversation. In some cases, processor 220 communicates an inquiry touser interface 204; and the processor is configured to process an answerto the inquiry in a following submission 212 from the user interface204. In some cases, an answer to an inquiry present within a submission212 from a user device 204 may be used by computing device 108 as aninput to another function, as described through this disclosure.

Referring now to FIG. 3 , an exemplary embodiment of a machine-learningmodule 300 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 304 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 308 given data provided as inputs 312;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 3 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 304 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 304 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 304 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 304 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 304 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 304 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data304 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 3 ,training data 304 may include one or more elements that are notcategorized; that is, training data 304 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 304 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 304 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 304 used by machine-learning module 300 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 3 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 316. Training data classifier 316 may include a classifierwhich as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 300 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 304. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 3 , machine-learning module 300 may beconfigured to perform a lazy-learning process 320 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 304. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 304 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 3 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 324. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 324 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 324 may be generated by creating an artificialneural network, such as a convolutional neural network including aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 304set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 3 , machine-learning algorithms may include atleast a supervised machine-learning process 328. At least a supervisedmachine-learning process 328, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs and outputs described through this disclosure, and ascoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 304. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 328 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 3 , machine learning processes may include atleast an unsupervised machine-learning processes 332. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 3 , machine-learning module 300 may be designedand configured to create a machine-learning model 324 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 4 , an exemplary embodiment of neural network 400is illustrated. A neural network 400 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 404, one or more intermediate layers 408, and an output layer ofnodes 412. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 5 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation, aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring to FIG. 6 , an exemplary embodiment of fuzzy set comparison600 is illustrated. A first fuzzy set 604 may be represented, withoutlimitation, according to a first membership function 608 representing aprobability that an input falling on a first range of values 612 is amember of the first fuzzy set 604, where the first membership function608 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function608 may represent a set of values within first fuzzy set 604. Althoughfirst range of values 612 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 612 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 608 mayinclude any suitable function mapping first range 612 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,\ {{{for}\ x} > {c\ {and}\ x} < a}} \\{\frac{x - a}{b - a},{{{for}{\ }a} \leq x < b}} \\{\frac{c - x}{c - b},\ {{{if}\ b} < x \leq c}}\end{matrix} \right.$a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\ \left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right)},0} \right)}$a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 6 , first fuzzy set 604 may represent any valueor combination of values as described above, including output from oneor more machine-learning models, classified vehicle data, and apredetermined class, such as without limitation of vehicle maintenanceactivity. A second fuzzy set 616, which may represent any value whichmay be represented by first fuzzy set 604, may be defined by a secondmembership function 620 on a second range 624; second range 624 may beidentical and/or overlap with first range 612 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 604 and second fuzzyset 616. Where first fuzzy set 604 and second fuzzy set 616 have aregion 628 that overlaps, first membership function 608 and secondmembership function 620 may intersect at a point 632 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 604 and second fuzzy set 616. Alternatively oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 636 on first range 612 and/or second range 624, wherea probability of membership may be taken by evaluation of firstmembership function 608 and/or second membership function 620 at thatrange point. A probability at 628 and/or 632 may be compared to athreshold 640 to determine whether a positive match is indicated.Threshold 640 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 604 and second fuzzy set 616, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or classified vehicle data and apredetermined class, such as without limitation vehicle maintenanceactivity categorization, for combination to occur as described above.Alternatively or additionally, each threshold may be tuned by amachine-learning and/or statistical process, for instance and withoutlimitation as described in further detail below.

Further referring to FIG. 6 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify an classified vehicle datawith vehicle maintenance activity. For instance, if a vehiclemaintenance activity has a fuzzy set matching classified vehicle datafuzzy set by having a degree of overlap exceeding a threshold, computingdevice may classify the classified vehicle data as belonging to thevehicle maintenance activity categorization. Where multiple fuzzymatches are performed, degrees of match for each respective fuzzy setmay be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match.

Still referring to FIG. 6 , in an embodiment, an classified vehicle datamay be compared to multiple vehicle maintenance activity categorizationfuzzy sets. For instance, classified vehicle data may be represented bya fuzzy set that is compared to each of the multiple vehicle maintenanceactivity categorization fuzzy sets; and a degree of overlap exceeding athreshold between the classified vehicle data fuzzy set and any of themultiple vehicle maintenance activity categorization fuzzy sets maycause computing device 108 to classify the classified vehicle data asbelonging to a vehicle maintenance activity categorization. Forinstance, in one embodiment there may be two vehicle maintenanceactivity categorization fuzzy sets, representing respectively a firstvehicle maintenance activity categorization and a second vehiclemaintenance activity categorization. First vehicle maintenance activitycategorization may have a first fuzzy set; Second vehicle maintenanceactivity categorization may have a second fuzzy set; and classifiedvehicle data may have an classified vehicle data fuzzy set. computingdevice 108, for example, may compare an classified vehicle data fuzzyset with each of vehicle maintenance activity categorization fuzzy setand in vehicle maintenance activity categorization fuzzy set, asdescribed above, and classify a classified vehicle data to either, both,or neither of first vehicle maintenance activity categorization nor insecond vehicle maintenance activity categorization. Machine-learningmethods as described throughout may, in a non-limiting example, generatecoefficients used in fuzzy set equations as described above, such aswithout limitation x, c, and σ of a Gaussian set as described above, asoutputs of machine-learning methods. Likewise, classified vehicle datamay be used indirectly to determine a fuzzy set, as classified vehicledata fuzzy set may be derived from outputs of one or moremachine-learning models that take the classified vehicle data directlyor indirectly as inputs.

Still referring to FIG. 6 , a computing device may use a logiccomparison program, such as, but not limited to, a fuzzy logic model todetermine a vehicle maintenance activity response. An vehiclemaintenance activity response may include, but is not limited to,inappropriate, good, average, optimal, and the like; each such vehiclemaintenance activity response may be represented as a value for alinguistic variable representing vehicle maintenance activity responseor in other words a fuzzy set as described above that corresponds to adegree of match calculated using any statistical, machine-learning, orother method that may occur to a person skilled in the art uponreviewing the entirety of this disclosure. In other words, a givenelement of classified vehicle data may have a first non-zero value formembership in a first linguistic variable value such as “beneficial” anda second non-zero value for membership in a second linguistic variablevalue such as “optimal” In some embodiments, determining a vehiclemaintenance activity categorization may include using a linearregression model. A linear regression model may include a machinelearning model. A linear regression model may be configured to map dataof classified vehicle data, such as degree of match to one or morevehicle maintenance activity parameters. A linear regression model maybe trained using a machine learning process. A linear regression modelmay map statistics such as, but not limited to, quality of classifiedvehicle data match. In some embodiments, determining an vehiclemaintenance activity of classified vehicle data may include using avehicle maintenance activity classification model. An vehiclemaintenance activity classification model may be configured to inputcollected data and cluster data to a centroid based on, but not limitedto, frequency of appearance, linguistic indicators of quality, and thelike. Centroids may include scores assigned to them such that quality ofmatch of classified vehicle data may each be assigned a score. In someembodiments vehicle maintenance activity classification model mayinclude a K-means clustering model. In some embodiments, vehiclemaintenance activity classification model may include a particle swarmoptimization model. In some embodiments, determining the vehiclemaintenance activity of an classified vehicle data may include using afuzzy inference engine. A fuzzy inference engine may be configured tomap one or more classified vehicle data elements using fuzzy logic. Insome embodiments, classified vehicle data may be arranged by a logicalcomparison program into vehicle maintenance activity arrangement. An“vehicle maintenance activity arrangement” used in this disclosure isany grouping of objects and/or data based on skill level and/or outputscore. This step may be implemented as described above in FIGS. 1-5 .Membership function coefficients and/or constants as described above maybe tuned according to classification and/or clustering algorithms. Forinstance, and without limitation, a clustering algorithm may determine aGaussian or other distribution of questions about a centroidcorresponding to a given match level, and an iterative or other methodmay be used to find a membership function, for any membership functiontype as described above, that minimizes an average error from thestatistically determined distribution, such that, for instance, atriangular or Gaussian membership function about a centroid representinga center of the distribution that most closely matches the distribution.Error functions to be minimized, and/or methods of minimization, may beperformed without limitation according to any error function and/orerror function minimization process and/or method as described in thisdisclosure.

Further referring to FIG. 6 , an inference engine may be implementedaccording to input and/or output membership functions and/or linguisticvariables. For instance, a first linguistic variable may represent afirst measurable value pertaining to classified vehicle data, such as adegree of match of an element, while a second membership function mayindicate a degree of benefit in vehicle maintenance activity of asubject thereof, or another measurable value pertaining to classifiedvehicle data. Continuing the example, an output linguistic variable mayrepresent, without limitation, a score value. An inference engine maycombine rules, such as: “if the match level is ‘high’ and the benefitlevel is ‘high’, the vehicle maintenance activity score is ‘high’”—thedegree to which a given input function membership matches a given rulemay be determined by a triangular norm or “T-norm” of the rule or outputmembership function with the input membership function, such as min (a,b), product of a and b, drastic product of a and b, Hamacher product ofa and b, or the like, satisfying the rules of commutativity (T(a,b)=T(b, a)), monotonicity: (T(a, b)≤T(c, d) if a≤c and b≤d),(associativity: T(a, T(b, c))=T(T(a, b), c)), and the requirement thatthe number 1 acts as an identity element. Combinations of rules (“and”or “or” combination of rule membership determinations) may be performedusing any T-conorm, as represented by an inverted T symbol or “⊥,” suchas max(a, b), probabilistic sum of a and b (a+b−a*b), bounded sum,and/or drastic T-conorm; any T-conorm may be used that satisfies theproperties of commutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)≤⊥(c,d) if a≤c and b≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), andidentity element of 0. Alternatively or additionally T-conorm may beapproximated by sum, as in a “product-sum” inference engine in whichT-norm is product and T-conorm is sum. A final output score or otherfuzzy inference output may be determined from an output membershipfunction as described above using any suitable defuzzification process,including without limitation Mean of Max defuzzification, Centroid ofArea/Center of Gravity defuzzification, Center Average defuzzification,Bisector of Area defuzzification, or the like. Alternatively oradditionally, output rules may be replaced with functions according tothe Takagi-Sugeno-King (TSK) fuzzy model.

Referring now to FIG. 7 , an flow diagram of a method 700 for generatinga vehicle maintenance activity. At step 705, method 700 includesreceiving, by a computing device, image data, for example, and asimplemented in FIGS. 1-6 . The image data may include a license plate ofvehicle. At step 710, method 700 includes identifying, by the computingdevice, at least an indicator from the image data, for example, and asimplemented in FIGS. 1-6 . The at least indicator may include a licenseplate number. At step 715, method 700 includes receiving, by thecomputing device, vehicle data as a function of the indicator, forexample, and as implemented in FIGS. 1-6 . The vehicle data may includea user identification. The vehicle data may include a vehicleidentification. The vehicle data may include service information. Theservice information may be updated as a function of the vehiclemaintenance activity. Receiving the vehicle data may include performinga verification function based on the image data and vehicle datareceived. At step 720, method 700 includes classifying, by the computingdevice, the vehicle data to a plurality of vehicle data categories, forexample, and as implemented in FIGS. 1-6 . Classifying the vehicle datamay include receiving a vehicle data training data set correlating anelement of user identification information to a vehicle data group,training a vehicle data classifier as a function of receiving thevehicle data training data set, and outputting, utilizing the vehicledata classifier, a plurality of vehicle data categories classified tothe vehicle data. At step 725, method 700 includes generating, by thecomputing device, a vehicle maintenance activity as a function of theclassified vehicle data, for example, and as implemented in FIGS. 1-6 .Generating the vehicle maintenance activity may include using a fuzzyset inference system.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), system on module (SOM), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatuses, systems, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for generating a vehicle maintenanceactivity, wherein the apparatus comprises: at least a processor; and amemory communicatively connected to the at least a processor, the memorycontaining instructions configuring the at least a processor to: receiveimage data; identify at least an indicator from the image data; receivevehicle data as a function of the indicator; classify the vehicle datato a plurality of vehicle data categories, wherein classifying thevehicle data further comprises: receiving a vehicle data training dataset correlating an element of user identification information to avehicle data group, wherein the user identification informationcomprises authorized user information; training a vehicle dataclassifier as a function of receiving the vehicle data training dataset; and outputting, utilizing the vehicle data classifier, a pluralityof vehicle data categories classified to the vehicle data using thevehicle data as an input; and generate a vehicle maintenance activity asa function of the classified vehicle data.
 2. The apparatus of claim 1,wherein the image data comprises a vehicle credential.
 3. The apparatusof claim 1, wherein the at least indicator comprises an alphanumericcredential associated within the vehicle.
 4. The apparatus of claim 1,wherein the vehicle data comprises a user identification.
 5. Theapparatus of claim 1, wherein the vehicle data comprises a vehicleidentification.
 6. The apparatus of claim 1, wherein the vehicle datacomprises service information.
 7. The apparatus of claim 6, wherein theservice information is updated as a function of the vehicle maintenanceactivity.
 8. The apparatus of claim 1, wherein the at least processor isfurther configured to perform a verification function based on the imagedata and vehicle data received.
 9. The apparatus of claim 1, whereingenerating the vehicle maintenance activity comprises using a fuzzy setinference system.
 10. A method for generating a vehicle maintenanceactivity, wherein the method comprises: receiving, by a computingdevice, image data; identifying, by the computing device, at least anindicator from the image data; receiving, by the computing device,vehicle data as a function of the indicator; classifying, by thecomputing device, the vehicle data to a plurality of vehicle datacategories, wherein classifying the vehicle data further comprises:receiving a vehicle data training data set correlating an element ofuser identification information to a vehicle data group, wherein theuser identification information comprises authorized user information;training a vehicle data classifier as a function of receiving thevehicle data training data set; and outputting, utilizing the vehicledata classifier, a plurality of vehicle data categories classified tothe vehicle data using the vehicle data as an input; and generating, bythe computing device, a vehicle maintenance activity as a function ofthe classified vehicle data.
 11. The method of claim 10, wherein theimage data comprises a vehicle credential.
 12. The method of claim 10,wherein the at least indicator comprises an alphanumeric credentialassociated within the vehicle.
 13. The method of claim 10, wherein thevehicle data comprises a user identification.
 14. The method of claim10, wherein the vehicle data comprises a vehicle identification.
 15. Themethod of claim 10, wherein the vehicle data comprises serviceinformation.
 16. The method of claim 15, wherein receiving the vehicledata further comprises updating the service information as a function ofthe vehicle maintenance activity.
 17. The method of claim 10, whereinreceiving the vehicle data comprises performing a verification functionbased on the image data and vehicle data received.
 18. The method ofclaim 10, wherein generating the vehicle maintenance activity comprisesusing a fuzzy set inference system.